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Purpose: Intravoxel incoherent motion (IVIM) magnetic resonance imaging is a potential noninvasive technique for the diagnosis of brain tumors. However, perfusion‐related parameter mapping is a persistent problem. The purpose of this paper is to investigate the IVIM parameter mapping of brain tumors using Bayesian fitting and low b‐values. Methods: Bayesian shrinkage prior (BSP) fitting method and different low b‐value distributions were used to estimate IVIM parameters (diffusion D, pseudo‐diffusion D*, and perfusion fraction F). The results were compared to those obtained by least squares (LSQ) on both simulated and in vivo brain data. Relative error (RE) and reproducibility were used to evaluate the results. The differences of IVIM parameters between brain tumor and normal regions were compared and used to assess the performance of Bayesian fitting in the IVIM application of brain tumor. Results: In tumor regions, the value of D* tended to be decreased when the number of low b‐values was insufficient, especially with LSQ. BSP required less low b‐values than LSQ for the correct estimation of perfusion parameters of brain tumors. The IVIM parameter maps of brain tumors yielded by BSP had smaller variability, lower RE, and higher reproducibility with respect to those obtained by LSQ. Obvious differences were observed between tumor and normal regions in parameters D (P < 0.05) and F (P < 0.001), especially F. BSP generated fewer outliers than LSQ, and distinguished better tumors from normal regions in parameter F. Conclusions: Intravoxel incoherent motion parameters clearly allow brain tumors to be differentiated from normal regions. Bayesian fitting yields robust IVIM parameter mapping with fewer outliers and requires less low b‐values than LSQ for the parameter estimation. [ABSTRACT FROM AUTHOR]

Intravoxel incoherent motion (IVIM) imaging is a magnetic resonance imaging (MRI) technique widely used in clinical applications for various organs. However, IVIM imaging at low b-values is a persistent problem. This paper aims to investigate in a systematic and detailed manner how the number of low b-values influences the estimation of IVIM parameters. To this end, diffusion-weighted (DW) data with different low b-values were simulated to get insight into the distributions of subsequent IVIM parameters. Then, in vivo DW data with different numbers of low b-values and different number of excitations (NEX) were acquired. Finally, least-squares (LSQ) and Bayesian shrinkage prior (BSP) fitting methods were implemented to estimate IVIM parameters. The influence of the number of low b-values on IVIM parameters was analyzed in terms of relative error (RE) and structural similarity (SSIM). The results showed that the influence of the number of low b-values on IVIM parameters is variable. LSQ is more dependent on the number of low b-values than BSP, but the latter is more sensitive to noise. [ABSTRACT FROM AUTHOR]

Gaussian mixture model (GMM) was proposed to depict the perfusion volume fraction distribution in the generalized intravoxel incoherent motion model (GIVIM) to improve GIVIM's ability of describing complex perfusion conditions and their changes. Different hepatic perfusion conditions were accounted for by performing different combinations of imaging sequence and diffusion time on six normal livers. In order to evaluate GIVIM‐GMM's reliability in perfusion condition analysis, the fitting to diffusion‐weighted (DW) data and the consistency between diffusion‐related parameters' change and the data's change were tested and the recent GIVIM and the triexponential models were chosen for comparison. The difference of the fitting results was evaluated by performing the extra‐sum‐of‐squares F test and information criteria on normal human DW data. The difference of the consistency was assessed by using two‐tailed paired Student's t test. In the extra‐sum‐of‐squares F test, the relative difference ratio F values derived from the GIVIM and GIVIM‐GMM and that derived from the triexponential model and the GIVIM‐GMM are respectively 25.334 and 27.976, which indicates that significant difference existed and that the GIVIM‐GMM provides better fit to the normal human liver DW data. In information criteria test, the evidence ratio values were determined by dividing the GIVIM's or triexponential model's correct probability by the GIVIM‐GMM's. Both evidence ratio values (2.3942 × 10−10, 8.6167 × 10−9, respectively) are much smaller than 1, which also expresses that the best model used to fit the normal human liver DW data was the GIVIM‐GMM. In two‐tailed paired student's t test, the GIVIM‐GMM provides more parameters to give a finer description of perfusion than the triexponential model or GIVIM. In short, all the results demonstrated that the GIVIM‐GMM provides better performance than the existing IVIM models for depicting the signal attenuation in DW imaging. [ABSTRACT FROM AUTHOR]